Caltech CS184a Fall DeHon1 CS184a: Computer Architecture (Structures and Organization) Day20: November 29, 2000 Review

Caltech CS184a Fall DeHon2 Today Review content and themes N.B. EOT Feedback Questionnaire –return end of class in basket –or later to Cynthia (256 JRG)

Caltech CS184a Fall DeHon3 Physical Implementation of Computation: Engineering Problem Implement a computation: –with least resources (in fixed resources) with least cost –in least time (in fixed time) –with least energy Optimization problem –how do we do it best

Caltech CS184a Fall DeHon4 Architecture Not Done Not here to just teach you the forms which are already understood –(though, will do that and give you a strong understanding of their strengths and weaknesses) Goal: enable you to design and synthesize new and better architectures Engineering not Biology

Caltech CS184a Fall DeHon5 Authority/History ``Science is the belief in the ignorance of experts.'' -- Richard Feynman Goal: Teach you to think critically and independently about computer design.

Caltech CS184a Fall DeHon6 Content Overview This quarter: –building blocks and organization –raw components and their consequences Next two quarters: –abstractions, models, techniques, systems –e.g. ISA, Control and Data Flow, caching, VM, processor pipeline, branching, renaming….RISC, VLIW, SuperScalar, Vector, SIMD, ….

Caltech CS184a Fall DeHon7 Content (this quarter) Requirements of Computation Key components: –Instructions –Interconnect –Compute –Retiming –Control

Caltech CS184a Fall DeHon8 Themes (this quarter) Implementation techniques Costs Structure in Computations Design Space –identify and model Parameterization Metrics and Figures of Merit Tradeoffs, analysis Change

Caltech CS184a Fall DeHon9 Computing Device Composition –Bit Processing elements –Interconnect: space –Interconnect: time –Instruction Memory Tile together to build device

Caltech CS184a Fall DeHon10 Peak Computational Densities from Model Small slice of space –only 2 parameters 100  density across Large difference in peak densities –large design space!

Caltech CS184a Fall DeHon11 Yielded Efficiency Large variation in yielded density –large design space! FPGA (c=w=1) “Processor” (c=1024, w=64)

Caltech CS184a Fall DeHon12 Throughput Yield Same graph, rotated to show backside.

Caltech CS184a Fall DeHon13 Architecture Instr. Taxonomy

Caltech CS184a Fall DeHon14 Methodology Architecture model (parameterized) Cost model Important task characteristics Mapping Algorithm –Map to determine resources Apply cost model Digest results –find optimum (multiple?) –understand conflicts (avoidable?)

Caltech CS184a Fall DeHon15 Mapped LUT Area

Caltech CS184a Fall DeHon16 Resources  Area Model  Area

Caltech CS184a Fall DeHon17 Control: Partitioning versus Contexts (Area) CSE benchmark

Caltech CS184a Fall DeHon18 Design Space Mindset Methodology Decomposition –fundamental building blocks –basis set Build Intuition on Space –grounded in quantifiable instances

Caltech CS184a Fall DeHon19 Change A key feature of the computer industry has been rapid and continual change. We must be prepared to adapt. For our substrate: –capacity (orders of magnitude more) what can put on die, parallelism, need for interconnect and virtualization, homogeneity –speed –relative delay of interconnect and gates

Caltech CS184a Fall DeHon20 Fountainhead Parthenon Quote “Look,” said Roark. “The famous flutings on the famous columns---what are they there for? To hide the joints in wood---when columns were made of wood, only these aren’t, they’re marble. The triglyphs, what are they? Wood. Wooden beams, the way they had to be laid when people began to build wooden shacks. Your Greeks took marble and they made copies of their wooden structures out of it, because others had done it that way. Then your masters of the Renaissance came along and made copies in plaster of copies in marble of copies in wood. Now here we are making copies in steel and concrete of copies in plaster of copies in marble of copies in wood. Why?”

Caltech CS184a Fall DeHon21 What About Computer Architecture? Are we making copies in submicron CMOS VLSI of copies in NMOS of copies in TTL of early vacuum tube computer designs? Mainframe->Mini->super microprocessors ? CDC->Cray1->i860->Vector microprocessors?

Caltech CS184a Fall DeHon Computer Architecture VLSI is “new” to the computer architect you have 15M   in 4  m NMOS want to run “all” programs What do you build?

Caltech CS184a Fall DeHon23 What can we build in 15M  12Kb SRAM (1.2K  bit) 1500 Gate-Array Gates (10K  /gate) 30 4-LUTs (500K /4LUT) 32b ALU+RF+control

Caltech CS184a Fall DeHon24 What…1983?

Caltech CS184a Fall DeHon25 More Why?

Caltech CS184a Fall DeHon26

Caltech CS184a Fall DeHon RISC II MIPs

Caltech CS184a Fall DeHon28 What has changed in 17 years? Technology (0.18  m CMOS) Capacity (50G  Architecture? 2

Caltech CS184a Fall DeHon29 Capacity

Caltech CS184a Fall DeHon30 Architecture (last 17 years) Moved memory system on chip 32->64b datapath +FPU, moved on chip 1->4 or 8 compute units …lots of “hacks” to preserve sequential model of original uP

Caltech CS184a Fall DeHon31 Have our assumptions changed? Beware of cached answers. Always check your assumptions. To stay young requires unceasing cultivation of the ability to unlearn old falsehoods. -- Lazarus Long

Caltech CS184a Fall DeHon Design Landscape

Caltech CS184a Fall DeHon33 Should we still build computers the way we did in 1983? Yesterday’s solution becomes today’s historical curiosity. -- Goldratt

Caltech CS184a Fall DeHon34 Example HP PA-RISC8500 (Hot Chips X) SPEC fits in on-chip cache What next? Does it make sense to keep this architecture and balance as capacity continues to grow? Hopefully, this class has given you some ideas of what else you could do with 100+G 2 …continue with next quarter...

Caltech CS184a Fall DeHon35 Also Ask... What happened in early 1980’s to make RISC possible / the right answer? –Compared to 70’s ?

Caltech CS184a Fall DeHon36 What do I want? Develop systematic design Parameterize design space –adapt to costs Understand/capture req. of computing Efficiency metrics –(similar to information theory?) –[related to last time: how much really need to compute]

Caltech CS184a Fall DeHon37 Big Ideas Matter Computes Efficiency of architectures varies widely Computation design is an engineering discipline Costs change  Best solutions (architectures) change Learn to cut through hype –analyze, think, critique, synthesize

Caltech CS184a Fall DeHon38 Big Ideas Design Space Effects of organization: –Instructions –Interconnect –Compute Block –Retiming –Control Key components of computing device